System, method, and computer program product for determining a control account that corresponds to an exposed account

ABSTRACT

Provided is a method including determining a combined plurality of accounts, determining, for each account of the combined plurality of accounts, aggregate transaction data associated with a plurality of transactions involving the account; determining a first cohort level group including a group of exposed accounts and controls accounts; determining a first segment level group of accounts from the first cohort level group of accounts; generating a prediction model based on a plurality of control accounts that are included in the first segment level group of accounts; identifying a first exposed account of a plurality of exposed accounts; determining a first control account of the plurality of control accounts that corresponds to a first exposed account using the prediction model; and outputting a report comprising data associated with the first control account that corresponds to the first exposed account. Systems and computer program products are also provided.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. patentapplication Ser. No. 16/820,074, filed on Mar. 16, 2020, which claimspriority to U.S. Provisional Patent Application No. 62/818,939, filed onMar. 15, 2019, the disclosures of which are hereby incorporated byreference in their entirety.

BACKGROUND 1. Field

This disclosure relates generally to event measurement and, in oneparticular embodiment, to a system, method, and computer program productdetermining a control account that corresponds to an exposed account.

2. Technical Considerations

A loyalty program (e.g., a rewards program, points program, and/or thelike) may include a structured marketing strategy to encourage customersto conduct transactions with a merchant associated with the loyaltyprogram. The structured marketing strategy may include a benefitprovided to the customers to encourage the customers to make purchasesof goods from the merchant and/or to use a service of the merchant. Aloyalty program may include a benefit provided based on types ofcommerce (e.g., in person transactions, e-commerce, and/or the like),and each type of commerce may have varying features and/or benefitschemes.

Further, a merchant may desire to measure an impact of an event on spendbehavior of an account or a set of accounts. The event may be defined asan action or activity that happens at a specific point in timeassociated with a loyalty program. For example, the event could be anemail offer associated with or enrollment in a loyalty program. However,to measure an impact of an event on spend behavior of an account or aset of accounts, it may be necessary to determine a control group and astudy group.

SUMMARY

The features and characteristics of the present disclosure, as well asthe methods of operation and functions of the related elements ofstructures and the combination of parts and economies of manufacture,will become more apparent upon consideration of the followingdescription with reference to the accompanying drawings, all of whichform a part of this specification, wherein like reference numeralsdesignate corresponding parts in the various figures. It is to beexpressly understood, however, that the drawings are for the purpose ofillustration and description only and are not intended as a definitionof the limits of the present disclosure. As used in the specification,the singular form of “a,” “an,” and “the” include plural referentsunless the context clearly dictates otherwise.

BRIEF DESCRIPTION OF THE DRAWINGS

Additional advantages and details of the present disclosure areexplained in greater detail below with reference to the exemplaryembodiments that are illustrated in the accompanying schematic figures,in which:

FIG. 1 is a diagram of a non-limiting embodiment of an environment inwhich systems, apparatuses, and/or methods, as described herein, may beimplemented;

FIG. 2 is a diagram of a non-limiting embodiment of components of one ormore devices of FIG. 1 ; and

FIG. 3 is a flowchart illustrating a non-limiting embodiment of a methodfor determining a control account that corresponds to an exposedaccount.

DETAILED DESCRIPTION

For purposes of the description hereinafter, the terms “end,” “upper,”“lower,” “right,” “left,” “vertical,” “horizontal,” “top,” “bottom,”“lateral,” “longitudinal,” and derivatives thereof shall relate to thedisclosure as it is oriented in the drawing figures. However, it is tobe understood that the disclosure may assume various alternativevariations and step sequences, except where expressly specified to thecontrary. It is also to be understood that the specific devices andprocesses illustrated in the attached drawings, and described in thefollowing specification, are simply exemplary embodiments or aspects ofthe disclosure. Hence, specific dimensions and other physicalcharacteristics related to the embodiments or aspects of the embodimentsdisclosed herein are not to be considered as limiting unless otherwiseindicated.

No aspect, component, element, structure, act, step, function,instruction, and/or the like used herein should be construed as criticalor essential unless explicitly described as such. In addition, as usedherein, the articles “a” and “an” are intended to include one or moreitems and may be used interchangeably with “one or more” and “at leastone.” Furthermore, as used herein, the term “set” is intended to includeone or more items (e.g., related items, unrelated items, a combinationof related and unrelated items, etc.) and may be used interchangeablywith “one or more” or “at least one.” Where only one item is intended,the term “one” or similar language is used. Also, as used herein, theterms “has,” “have,” “having,” or the like are intended to be open-endedterms. Further, the phrase “based on” is intended to mean “based atleast partially on” unless explicitly stated otherwise.

As used herein, the terms “communication” and “communicate” may refer tothe reception, receipt, transmission, transfer, provision, and/or thelike of information (e.g., data, signals, messages, instructions,commands, and/or the like). For one unit (e.g., a device, a system, acomponent of a device or system, combinations thereof, and/or the like)to be in communication with another unit means that the one unit is ableto directly or indirectly receive information from and/or send (e.g.,transmit) information to the other unit. This may refer to a direct orindirect connection that is wired and/or wireless in nature.Additionally, two units may be in communication with each other eventhough the information transmitted may be modified, processed, relayed,and/or routed between the first and second unit. For example, a firstunit may be in communication with a second unit even though the firstunit passively receives information and does not actively transmitinformation to the second unit. As another example, a first unit may bein communication with a second unit if at least one intermediary unit(e.g., a third unit located between the first unit and the second unit)processes information received from the first unit and transmits theprocessed information to the second unit. In some non-limitingembodiments or aspects, a message may refer to a network packet (e.g., adata packet and/or the like) that includes data.

As used herein, the terms “issuer,” “issuer institution,” “issuer bank,”or “payment device issuer” may refer to one or more entities thatprovide accounts to individuals (e.g., users, customers, and/or thelike) for conducting payment transactions, such as credit paymenttransactions and/or debit payment transactions. For example, an issuerinstitution may provide an account identifier, such as a primary accountnumber (PAN), to a customer that uniquely identifies one or moreaccounts associated with that customer. In some non-limiting embodimentsor aspects, an issuer may be associated with a bank identificationnumber (BIN) that uniquely identifies the issuer institution. As usedherein, the term “issuer system” may refer to one or more computersystems operated by or on behalf of an issuer, such as a serverexecuting one or more software applications. For example, an issuersystem may include one or more authorization servers for authorizing atransaction.

As used herein, the term “account identifier” may refer to one or moretypes of identifiers associated with an account (e.g., a PAN associatedwith an account, a card number associated with an account, a paymentcard number associated with an account, a token associated with anaccount, and/or the like). In some non-limiting embodiments or aspects,an issuer may provide an account identifier (e.g., a PAN, a token,and/or the like) to a user (e.g., an accountholder) that uniquelyidentifies one or more accounts associated with that user. The accountidentifier may be embodied on a payment device (e.g., a physicalinstrument used for conducting payment transactions, such as a paymentcard, a credit card, a debit card, a gift card, and/or the like) and/ormay be electronic information communicated to the user that the user mayuse for electronic payment transactions. In some non-limitingembodiments or aspects, the account identifier may be an originalaccount identifier, where the original account identifier was providedto a user at the creation of the account associated with the accountidentifier. In some non-limiting embodiments or aspects, the accountidentifier may be a supplemental account identifier, which may includean account identifier provided to a user after the original accountidentifier was provided to the user. For example, if the originalaccount identifier is forgotten, stolen, and/or the like, a supplementalaccount identifier may be provided to the user. In some non-limitingembodiments or aspects, an account identifier may be directly orindirectly associated with an issuer institution such that an accountidentifier may be a token that maps to a PAN or other type of accountidentifier. Account identifiers may be alphanumeric, any combination ofcharacters and/or symbols, and/or the like.

As used herein, the term “token” may refer to an account identifier usedas a substitute or replacement for another account identifier, such as aPAN. Tokens may be associated with a PAN or other original accountidentifier in one or more data structures (e.g., one or more databasesand/or the like) such that they may be used to conduct a paymenttransaction without directly using the original account identifier. Insome non-limiting embodiments or aspects, an original accountidentifier, such as a PAN, may be associated with a plurality of tokensfor different individuals or purposes. In some non-limiting embodimentsor aspects, tokens may be associated with a PAN or other accountidentifiers in one or more data structures such that they can be used toconduct a transaction without directly using the PAN or the otheraccount identifiers. In some examples, an account identifier, such as aPAN, may be associated with a plurality of tokens for different uses ordifferent purposes.

As used herein, the term “merchant” may refer to one or more entities(e.g., operators of retail businesses) that provide goods and/orservices, and/or access to goods and/or services, to a user (e.g., acustomer, a consumer, and/or the like) based on a transaction, such as apayment transaction. As used herein, the term “merchant system” mayrefer to one or more computer systems operated by or on behalf of amerchant, such as a server executing one or more software applications.As used herein, the term “product” may refer to one or more goods and/orservices offered by a merchant.

As used herein, the term “point-of-sale (POS) device” may refer to oneor more devices, which may be used by a merchant to conduct atransaction (e.g., a payment transaction) and/or process a transaction.For example, a POS device may include one or more client devices.Additionally or alternatively, a POS device may include peripheraldevices, card readers, scanning devices (e.g., code scanners),Bluetooth® communication receivers, near-field communication (NFC)receivers, radio frequency identification (RFID) receivers, and/or othercontactless transceivers or receivers, contact-based receivers, paymentterminals, and/or the like.

As used herein, the term “point-of-sale (POS) system” may refer to oneor more client devices and/or peripheral devices used by a merchant toconduct a transaction. For example, a POS system may include one or morePOS devices and/or other like devices that may be used to conduct apayment transaction. In some non-limiting embodiments or aspects, a POSsystem (e.g., a merchant POS system) may include one or more servercomputers programmed or configured to process online paymenttransactions through webpages, mobile device applications, and/or thelike.

As used herein, the term “transaction service provider” may refer to anentity that receives transaction authorization requests from merchantsor other entities and provides guarantees of payment, in some casesthrough an agreement between the transaction service provider and anissuer institution. For example, a transaction service provider mayinclude a payment network such as Visa®, MasterCard®, American Express®,or any other entity that processes transactions. As used herein, theterm “transaction service provider system” may refer to one or morecomputer systems operated by or on behalf of a transaction serviceprovider, such as a transaction service provider system executing one ormore software applications. A transaction service provider system mayinclude one or more processors and, in some non-limiting embodiments oraspects, may be operated by or on behalf of a transaction serviceprovider.

As used herein, the term “acquirer” may refer to an entity licensed bythe transaction service provider and approved by the transaction serviceprovider to originate transactions (e.g., payment transactions)involving a payment device associated with the transaction serviceprovider. As used herein, the term “acquirer system” may also refer toone or more computer systems, computer devices, and/or the like operatedby or on behalf of an acquirer. The transactions the acquirer mayoriginate may include payment transactions (e.g., purchases, originalcredit transactions (OCTs), account funding transactions (AFTs), and/orthe like). In some non-limiting embodiments or aspects, the acquirer maybe authorized by the transaction service provider to assign merchant orservice providers to originate transactions involving a payment deviceassociated with the transaction service provider. The acquirer maycontract with payment facilitators to enable the payment facilitators tosponsor merchants. The acquirer may monitor compliance of the paymentfacilitators in accordance with regulations of the transaction serviceprovider. The acquirer may conduct due diligence of the paymentfacilitators and ensure proper due diligence occurs before signing asponsored merchant. The acquirer may be liable for all transactionservice provider programs that the acquirer operates or sponsors. Theacquirer may be responsible for the acts of the acquirer's paymentfacilitators, merchants that are sponsored by the acquirer's paymentfacilitators, and/or the like. In some non-limiting embodiments oraspects, an acquirer may be a financial institution, such as a bank.

As used herein, the term “payment gateway” may refer to an entity and/ora payment processing system operated by or on behalf of such an entity(e.g., a merchant service provider, a payment service provider, apayment facilitator, a payment facilitator that contracts with anacquirer, a payment aggregator, and/or the like), which provides paymentservices (e.g., transaction service provider payment services, paymentprocessing services, and/or the like) to one or more merchants. Thepayment services may be associated with the use of portable paymentdevices managed by a transaction service provider. As used herein, theterm “payment gateway system” may refer to one or more computer systems,computer devices, servers, groups of servers, and/or the like operatedby or on behalf of a payment gateway.

As used herein, the terms “electronic wallet,” “electronic wallet mobiledevice application,” and “digital wallet” may refer to one or moreelectronic devices including one or more software applicationsconfigured to facilitate and/or conduct transactions (e.g., paymenttransactions, electronic payment transactions, and/or the like). Forexample, an electronic wallet may include a user device (e.g., a mobiledevice) executing an application program, server-side software, and/ordatabases for maintaining and providing data to be used during a paymenttransaction to the user device. As used herein, the term “electronicwallet provider” may include an entity that provides and/or maintains anelectronic wallet and/or an electronic wallet mobile device applicationfor a user (e.g., a customer). Examples of an electronic wallet providerinclude, but are not limited to, Google Pay®, Android Pay®, Apple Pay®,and Samsung Pay®. In some non-limiting examples, a financial institution(e.g., an issuer institution) may be an electronic wallet provider. Asused herein, the term “electronic wallet provider system” may refer toone or more computer systems, computer devices, servers, groups ofservers, and/or the like operated by or on behalf of an electronicwallet provider.

As used herein, the term “payment device” may refer to a payment card(e.g., a credit or debit card), a gift card, a smartcard, smart media, apayroll card, a healthcare card, a wristband, a machine-readable mediumcontaining account information, a keychain device or fob, an RFIDtransponder, a retailer discount or loyalty card, and/or the like. Thepayment device may include a volatile or a non-volatile memory to storeinformation (e.g., an account identifier, a name of the account holder,and/or the like).

As used herein, the terms “client” and “client device” may refer to oneor more devices, such as processors, storage devices, and/or similarcomponents, that access a service made available by a server. In somenon-limiting embodiments or aspects, a “client device” may refer to oneor more devices that facilitate payment transactions, such as POSdevices and/or POS systems used by a merchant. In some non-limitingembodiments or aspects, a client device may include an electronic deviceconfigured to communicate with one or more networks and/or facilitatepayment transactions such as, but not limited to, one or more desktopcomputers, one or more portable computers (e.g., tablet computers), oneor more mobile devices (e.g., cellular phones, smartphones, personaldigital assistants (PDAs), wearable devices, such as watches, glasses,lenses, and/or clothing, and/or the like), and/or other like devices.Moreover, a “client” may also refer to an entity, such as a merchant,that owns, utilizes, and/or operates a client device for facilitatingpayment transactions with a transaction service provider.

As used herein, the term “server” may refer to one or more devices, suchas processors, storage devices, and/or similar computer components thatcommunicate with client devices and/or other devices over a network,such as the Internet or private networks and, in some examples,facilitate communication among other servers and/or client devices.

As used herein, the term “system” may refer to one or more devices orcombinations of devices such as, but not limited to, processors,servers, client devices, software applications, and/or other likecomponents. In addition, reference to “a server” or “a processor,” asused herein, may refer to a previously-recited server and/or processorrecited as performing a previous step or function, a different serverand/or processor, and/or a combination of servers and/or processors. Forexample, as used in the specification and the claims, a first serverand/or a first processor that are recited as performing a first step orfunction may refer to the same or different server and/or a processorrecited as performing a second step or function.

As used herein, satisfying a threshold may refer to a value beinggreater than the threshold, more than the threshold, higher than thethreshold, greater than or equal to the threshold, less than thethreshold, fewer than the threshold, lower than the threshold, less thanor equal to the threshold, equal to the threshold, and/or the like.

Non-limiting embodiments of systems, methods, and computer programproducts described herein may enable the determination of a controlaccount that corresponds to an account that was exposed to an eventassociated with a merchant. For example, embodiments described hereinmay allow for more accuracy and/or more efficiency (e.g., using lessnetwork resources) when identifying control accounts that correspond toaccounts that were exposed to an event associated with a merchant ascompared to a process that involves only comparing one or more fields ofa historical transaction profile of an individual to one or more fieldsof a historical transaction profile of another individual.

Referring now to FIG. 1 , FIG. 1 is a diagram of an example environment100 in which devices, systems, and/or methods, described herein, may beimplemented. As shown in FIG. 1 , environment 100 includes transactionservice provider system 102, issuer system 104, user device 106,merchant system 108, acquirer system 110, and communication network 112.

Transaction service provider system 102 may include one or more devicescapable of receiving information from and/or communicating informationto issuer system 104, user device 106, merchant system 108, and/oracquirer system 110 via communication network 112. For example,transaction service provider system 102 may include a device, such as aserver (e.g., a transaction processing server), a group of servers,and/or other like devices. In some non-limiting embodiments, transactionservice provider system 102 may be associated with a transaction serviceprovider as described herein. In some non-limiting embodiments,transaction service provider system 102 may be in communication with adata storage device, which may be local or remote to the transactionservice provider system 102. In some non-limiting embodiments,transaction service provider system 102 may be capable of receivinginformation from, storing information in, communicating information to,or searching information stored in a data storage device.

Issuer system 104 may include one or more devices capable of receivinginformation and/or communicating information to transaction serviceprovider system 102, user device 106, merchant system 108, and/oracquirer system 110 via communication network 112. For example, issuersystem 104 may include a device, such as a server, a group of servers,and/or other like devices. In some non-limiting embodiments, issuersystem 104 may be associated with an issuer institution as describedherein. For example, issuer system 104 may be associated with an issuerinstitution that issued a credit account, debit account, credit card,debit card, and/or the like to a user associated with user device 106.

User device 106 may include one or more devices capable of receivinginformation from and/or communicating information to transaction serviceprovider system 102, issuer system 104, merchant system 108, and/oracquirer system 110 via communication network 112. For example, userdevice 106 may include a client device and/or the like. In somenon-limiting embodiments, user device 106 may or may not be capable ofreceiving information (e.g., from merchant system 108) via a short rangewireless communication connection (e.g., an NFC communicationconnection, an RFID communication connection, a Bluetooth® communicationconnection, and/or the like), and/or communicating information (e.g., tomerchant system 108) via a short-range wireless communicationconnection.

Merchant system 108 may include one or more devices capable of receivinginformation from and/or communicating information to transaction serviceprovider system 102, issuer system 104, user device 106, and/or acquirersystem 110 via communication network 112. Merchant system 108 may alsoinclude a device capable of receiving information from user device 106via communication network 112, a communication connection (e.g., an NFCcommunication connection, an RFID communication connection, a Bluetooth®communication connection, and/or the like) with user device 106, and/orthe like, and/or communicating information to user device 106 via thenetwork, the communication connection, and/or the like. In somenon-limiting embodiments, merchant system 108 may include a device, suchas a server, a group of servers, a client device, a group of clientdevices, and/or other like devices. In some non-limiting embodiments,merchant system 108 may be associated with a merchant as describedherein. In some non-limiting embodiments, merchant system 108 mayinclude one or more user devices 106. For example, merchant system 108may include user device 106 that allows a merchant to communicateinformation to transaction service provider system 102. In somenon-limiting embodiments, merchant system 108 may include one or moredevices, such as computers, computer systems, and/or peripheral devicescapable of being used by a merchant to conduct a transaction with auser. For example, merchant system 108 may include a POS device and/or aPOS system.

Acquirer system 110 may include one or more devices capable of receivinginformation from and/or communicating information to transaction serviceprovider system 102, issuer system 104, user device 106, and/or merchantsystem 108 via communication network 112. For example, acquirer system110 may include a device, a server, a group of servers, and/or the like.In some non-limiting embodiments, acquirer system 110 may be associatedwith an acquirer as described herein.

Communication network 112 may include one or more wired and/or wirelessnetworks. For example, communication network 112 may include a cellularnetwork (e.g., a long-term evolution (LTE) network, a third generation(3G) network, a fourth generation (4G) network, a code division multipleaccess (CDMA) network, etc.), a public land mobile network (PLMN), alocal area network (LAN), a wide area network (WAN), a metropolitan areanetwork (MAN), a telephone network (e.g., the public switched telephonenetwork (PSTN)), a private network, an ad hoc network, an intranet, theInternet, a fiber optic-based network, a cloud computing network, and/orthe like, and/or a combination of these or other types of networks.

The number and arrangement of devices and networks shown in FIG. 1 areprovided as an example. There may be additional devices, systems, and/ornetworks, fewer devices, systems, and/or networks, different devices,systems, and/or networks, or differently arranged devices, systems,and/or networks than those shown in FIG. 1 . Furthermore, two or moredevices or systems shown in FIG. 1 may be implemented within a singledevice or a single system, or a single device or a single system shownin FIG. 1 may be implemented as multiple, distributed devices ormultiple, distributed systems. Additionally or alternatively, a set ofdevices or systems (e.g., one or more devices or systems) of environment100 may perform one or more functions described as being performed byanother set of devices or systems of environment 100.

Referring now to FIG. 2 , FIG. 2 is a diagram of example components of adevice 200. Device 200 may correspond to one or more devices oftransaction service provider system 102, and/or one or more devices ofissuer system 104, user device 106, one or more devices of merchantsystem 108, and/or one or more devices of acquirer system 110. In somenon-limiting embodiments, transaction service provider system 102,issuer system 104, user device 106, merchant system 108, and/or acquirersystem 110 may include at least one device 200 and/or at least onecomponent of device 200. As shown in FIG. 2 , device 200 may include bus202, processor 204, memory 206, storage component 208, input component210, output component 212, and communication interface 214.

Bus 202 may include a component that permits communication among thecomponents of device 200. In some non-limiting embodiments, processor204 may be implemented in hardware, software, or a combination ofhardware and software. For example, processor 204 may include aprocessor (e.g., a central processing unit (CPU), a graphics processingunit (GPU), an accelerated processing unit (APU), etc.), amicroprocessor, a digital signal processor (DSP), and/or any processingcomponent (e.g., a field-programmable gate array (FPGA), anapplication-specific integrated circuit (ASIC), etc.) that can beprogrammed to perform a function. Memory 206 may include random accessmemory (RAM), read only memory (ROM), and/or another type of dynamic orstatic storage device (e.g., flash memory, magnetic memory, opticalmemory, etc.) that stores information and/or instructions for use byprocessor 204.

Storage component 208 may store information and/or software related tothe operation and use of device 200. For example, storage component 208may include a hard disk (e.g., a magnetic disk, an optical disk, amagneto-optic disk, a solid state disk, etc.), a compact disc (CD), adigital versatile disc (DVD), a floppy disk, a cartridge, a magnetictape, and/or another type of computer-readable medium, along with acorresponding drive.

Input component 210 may include a component that permits device 200 toreceive information, such as via user input (e.g., a touch screendisplay, a keyboard, a keypad, a mouse, a button, a switch, amicrophone, etc.). Additionally, or alternatively, input component 210may include a sensor for sensing information (e.g., a global positioningsystem (GPS) component, an accelerometer, a gyroscope, an actuator,etc.). Output component 212 may include a component that provides outputinformation from device 200 (e.g., a display, a speaker, one or morelight-emitting diodes (LEDs), etc.).

Communication interface 214 may include a transceiver-like component(e.g., a transceiver, a separate receiver and transmitter, etc.) thatenables device 200 to communicate with other devices, such as via awired connection, a wireless connection, or a combination of wired andwireless connections. Communication interface 214 may permit device 200to receive information from another device and/or provide information toanother device. For example, communication interface 214 may include anEthernet interface, an optical interface, a coaxial interface, aninfrared interface, a radio frequency (RF) interface, a universal serialbus (USB) interface, a Wi-Fi® interface, a cellular network interface,and/or the like.

Device 200 may perform one or more processes described herein. Device200 may perform these processes based on processor 204 executingsoftware instructions stored by a computer-readable medium, such asmemory 206 and/or storage component 208. A computer-readable medium(e.g., a non-transitory computer-readable medium) is defined herein as anon-transitory memory device. A memory device includes memory spacelocated inside of a single physical storage device or memory spacespread across multiple physical storage devices.

Software instructions may be read into memory 206 and/or storagecomponent 208 from another computer-readable medium or from anotherdevice via communication interface 214. When executed, softwareinstructions stored in memory 206 and/or storage component 208 may causeprocessor 204 to perform one or more processes described herein.Additionally or alternatively, hardwired circuitry may be used in placeof or in combination with software instructions to perform one or moreprocesses described herein. Thus, embodiments described herein are notlimited to any specific combination of hardware circuitry and software.

The number and arrangement of components shown in FIG. 2 are provided asan example. In some non-limiting embodiments, device 200 may includeadditional components, fewer components, different components, ordifferently arranged components than those shown in FIG. 2 .Additionally or alternatively, a set of components (e.g., one or morecomponents) of device 200 may perform one or more functions described asbeing performed by another set of components of device 200.

Referring now to FIG. 3 , FIG. 3 is a flowchart of a non-limitingembodiment of a process 300 for determining a control account thatcorresponds to an exposed account. In some non-limiting embodiments, oneor more of the steps of process 300 may be performed (e.g., completely,partially, etc.) by transaction service provider system 102 (e.g., oneor more devices of transaction service provider system 102). In somenon-limiting embodiments, one or more of the steps of process 300 may beperformed (e.g., completely, partially, etc.) by another device or agroup of devices separate from or including transaction service providersystem 102, such as issuer system 104 (e.g., one or more devices ofissuer system 104), user device 106, merchant system 108 (e.g., one ormore devices of merchant system 108), or acquirer system 110 (e.g., oneor more devices of acquirer system 110).

As shown in FIG. 3 , at step 302, process 300 may include determining acombined plurality of accounts. For example, transaction serviceprovider system 102 may determine a combined plurality of accounts. Insome non-limiting embodiments or aspects, transaction service providersystem 102 may determine a combined plurality of accounts including aplurality of exposed accounts and a plurality of control accounts. Forexample, transaction service provider system 102 may determine acombined plurality of accounts including a plurality of exposed accountsand a plurality of control accounts based on transaction serviceprovider system 102 combining the plurality of exposed accounts and theplurality of control accounts. In such an example, transaction serviceprovider system 102 may determine the combined plurality of accountsincluding each account of the plurality of exposed accounts and eachaccount of the plurality of control accounts based on transactionservice provider system 102 combining each of the plurality of exposedaccounts and each of the plurality of control accounts. In somenon-limiting embodiments or aspects, transaction service provider system102 may create the account pool table, described below. For example,transaction service provider system 102 may create the account pooltable, where the account pool table includes each account of a combinedplurality of accounts. In such an example, the combined plurality ofaccounts may include a plurality of control accounts and a plurality ofexposed accounts.

In some non-limiting embodiments or aspects, transaction serviceprovider system 102 may determine a plurality of exposed accounts. Forexample, transaction service provider system 102 may determine aplurality of exposed accounts where the plurality of exposed accountsinclude one or more groups (e.g., sets, pools, bundles, and/or the like)of accounts. In some non-limiting embodiments or aspects, the pluralityof exposed accounts (e.g., one or more groups of accounts of theplurality of exposed accounts) may be defined based on a time periodassociated with an event, such as a time period in which each account(e.g., each account of a group) was exposed to an event involving amerchant. In some non-limiting embodiments or aspects, an event may beassociated with a time period in which one or more accounts were exposedto the event involving the merchant, such as a time period during whichthe one or more accounts were provided an offer for redemption from themerchant. In some non-limiting embodiments or aspects, transactionservice provider system 102 may create a table of exposed accounts thatincludes one or more groups of accounts of a plurality of exposedaccounts.

In some non-limiting embodiments or aspects, transaction serviceprovider system 102 may determine a plurality of control accounts (e.g.,a plurality of individual accounts, a group of accounts, a plurality ofgroups of accounts, and/or the like). For example, transaction serviceprovider system 102 may determine a plurality of control accounts wherethe plurality of control accounts include one or more groups ofaccounts. In some non-limiting embodiments or aspects, each account ofthe plurality of control accounts may have been involved in at least onetransaction involving a merchant (e.g., a merchant associated with anevent, such as a merchant that provides an offer for redemption) duringone or more event time periods. In some non-limiting embodiments oraspects, transaction service provider system 102 may create a table ofcontrol accounts that includes one or more groups of accounts of aplurality of control accounts.

In some non-limiting embodiments or aspects, transaction serviceprovider system 102 may determine the combined plurality of accountsbased on transaction service provider system 102 combining a table ofexposed accounts and a table of control accounts. In some non-limitingembodiments or aspects, transaction service provider system 102 maycreate the account pool table based on the table of exposed accounts andthe table of control accounts. For example, transaction service providersystem 102 may create the account pool table based on transactionservice provider system 102 combining one or more accounts included inthe table of exposed accounts and one or more accounts included in thetable of control accounts.

In some non-limiting embodiments, transaction service provider system102 may determine a distance to a merchant location of a merchant (e.g.,a merchant associated with an event) that is closest to an account(e.g., an address associated with an account) for each account in theaccount pool table. In some non-limiting embodiments, the distance mayinclude a distance between a zip code of the account of a customer and azip code of the merchant location that is closest to the account of thecustomer. In some non-limiting embodiments or aspects, transactionservice provider system 102 may create an account pool table based ontransaction service provider system 102 determining one or moredistances to one or more merchant locations that are closest to anaccount for each account in the account pool table and/or for eachaccount in a plurality of groups of accounts.

In some non-limiting embodiments or aspects, transaction serviceprovider system 102 may perform a check for each account of the accountpool table and/or for each account of the plurality of groups ofaccounts (e.g., each account of the combined plurality of accounts). Forexample, transaction service provider system 102 may perform a checkbased on the distance between a merchant location of the merchant and azip code of an account of a customer of each account of the account pooltable and/or for accounts of the plurality of groups of accounts. Forexample, transaction service provider system 102 may perform a checkaccount of the account pool table and/or for accounts of the pluralityof groups of accounts based on transaction service provider system 102comparing the distance between the merchant location of the merchant andthe zip code of the account of a customer of each account to a thresholdvalue of distance. In such an example, the threshold value of distancemay be associated with a distance according to which one or more eventsare determined to be effective (e.g., to cause a customer of an accountto initiate a transaction in accordance with the event). In somenon-limiting embodiments or aspects, where the distance between themerchant location of the merchant and the zip code of the account of acustomer of each account satisfies the threshold value of distance,transaction service provider system 102 may include the account in thecombined plurality of accounts. Additionally, or alternatively, wherethe distance between the merchant location of the merchant and the zipcode of the account of a customer of each account does not satisfy thethreshold value of distance, transaction service provider system 102 mayforego including the account in the combined plurality of accounts.

As shown in FIG. 3 , at step 304, process 300 may include determining,for each account, aggregate transaction data associated with a pluralityof transactions involving the account. For example, transaction serviceprovider system 102 may determine, for each account, aggregatetransaction data associated with a plurality of transactions involvingthe account. In such an example, transaction service provider system 102may determine the aggregate transaction data associated with a pluralityof transactions involving the account, where each transaction involvesan account of the combined plurality of accounts. In some non-limitingembodiments or aspects, each transaction of the plurality oftransactions may be associated with (e.g., conducted during) an eventtime period. In some non-limiting embodiments or aspects, eachtransaction of the plurality of transactions may include data associatedwith a plurality of transaction variables. For example, data associatedwith each transaction of the plurality of transactions may include dataassociated with a plurality of transaction variables.

In some non-limiting embodiments or aspects, transaction serviceprovider system 102 may create a monthly aggregate transaction datatable. For example, transaction service provider system 102 may create amonthly aggregate transaction data table for each account of thecombined plurality of accounts. In some non-limiting embodiments oraspects, transaction service provider system 102 may create the monthlyaggregate transaction data table for each account of the combinedplurality of accounts based on transaction service provider system 102generating the aggregate transaction data associated with the pluralityof transactions. For example, transaction service provider system 102may create the monthly aggregate transaction data table for each accountof the combined plurality of accounts based on transaction serviceprovider system 102 retrieving data associated with one or moretransactions from a database to create the aggregate transaction datatable. In some non-limiting embodiments or aspects, transaction serviceprovider system 102 may determine an initial specified event timeperiod. For example, transaction service provider system 102 maydetermine an initial specified event time period that is a firstspecified event time period of a plurality of event time periods.Additionally, or alternatively, transaction service provider system 102may determine the initial specified event time period that is a secondspecified event time period of a plurality of event time periods, wherethe second specified event time period is a time period that is afterthe initial specified event time period. In some non-limitingembodiments or aspects, transaction service provider system 102 maydetermine a final specified event time period. For example, transactionservice provider system 102 may determine a final specified event timeperiod that is a final specified event time period (e.g., the mostrecent specified event time period) of the plurality of specified eventtime periods.

In some non-limiting embodiments or aspects, transaction serviceprovider system 102 may determine aggregate transaction data associatedwith a plurality of transactions during a time interval (e.g., aplurality of time periods). For example, transaction service providersystem 102 may determine aggregate transaction data associated with aplurality of transactions during a time interval that is associated withan initial specified event time period and a final specified event timeperiod. In such an example, transaction service provider system 102 maydetermine the aggregate transaction data associated with a plurality oftransactions during the time interval that is associated with theinitial specified event time period and the final specified event timeperiod, where each transaction of the plurality of transactions isassociated with (e.g., corresponds to, involves, is conducted using,and/or the like) an account (e.g., an account of the combined pluralityof accounts). In some non-limiting embodiments or aspects, transactionservice provider system 102 may determine the aggregate transaction dataassociated with a plurality of transactions during a time interval for aplurality of accounts. For example, transaction service provider system102 may determine the aggregate transaction data associated with theplurality of transactions during a time interval for a plurality ofaccounts of the combined plurality of accounts. In an example,transaction service provider system 102 may determine aggregatetransaction data associated with the plurality of transactions during atime interval starting fifteen months prior to an initial specifiedevent time period and ending six months after a final specified eventtime period for each account of the combined plurality of accounts.

As further shown in FIG. 3 , at step 306, process 300 may includedetermining a cohort level group. For example, transaction serviceprovider system 102 may determine a cohort level group (e.g., a cohortlevel group of accounts). In some non-limiting embodiments or aspects,transaction service provider system 102 may determine one or more cohortlevel groups (e.g., a plurality of cohort level groups, a predeterminednumber of cohort level groups, and/or the like). In some non-limitingembodiments or aspects, transaction service provider system 102 maydetermine the first cohort level group based on a specified event timeperiod. For example, transaction service provider system 102 maydetermine the first cohort level group where the first cohort levelgroup includes accounts that were exposed to an event associated with amerchant during the specified time period. In such an example, the firstcohort level group includes accounts that were exposed to an eventassociated with a merchant during the specified time period during whichaccounts of the combined plurality of accounts enrolled in a loyaltyprogram.

In some non-limiting embodiments or aspects, transaction serviceprovider system 102 may create a cohort level table. In somenon-limiting embodiments, transaction service provider system 102 maycreate the cohort level table based on one or more cohort groups ofaccounts. For example, transaction service provider system 102 maycreate the cohort level table based on one or more cohort groups ofaccounts, where each first cohort level group includes a plurality ofaccounts that are included in one or more of the cohort groups ofaccounts.

In some non-limiting embodiments or aspects, transaction serviceprovider system 102 may determine cohort aggregate data associated withone or more cohort groups of accounts. For example, transaction serviceprovider system 102 may determine cohort aggregate data associated withone or more cohort groups of accounts, where the cohort aggregate datafor each of the one or more cohort groups of accounts includes cohortaggregate transaction data associated with a plurality of transactions.In such an example, the cohort aggregate transaction data may includeaggregate transaction data associated with a plurality of transactionsduring a specified event time period, the plurality of transactions alsobeing associated with (e.g., corresponds to, conducted by, and/or thelike) one or more accounts (e.g., one or more control accounts and/orone or more exposed accounts) included with the one or more cohortgroups of accounts. In some non-limiting embodiments or aspects,transaction service provider system 102 may determine the cohortaggregate transaction data associated with each first cohort level group(e.g., each first cohort level group of a plurality of cohort levelgroups of accounts). In some non-limiting embodiments or aspects,transaction service provider system 102 may determine (e.g., select) theaggregate transaction data associated with the plurality of transactionsduring the specified event time period that is associated with the oneor more cohort groups of accounts based on (e.g., from) the aggregatetransaction data associated with a plurality of transactions based on(e.g., during) a time interval. In some non-limiting embodiments oraspects, transaction service provider system 102 may determine thecohort aggregate transaction data associated with the plurality oftransactions during the specified event time period that is associatedwith a first cohort level group based on (e.g., from) the aggregatetransaction data associated with a plurality of transactions based on(e.g., during) the event time period for each account of the combinedplurality of accounts.

In some non-limiting embodiments or aspects, transaction serviceprovider system 102 may create a cohort monthly aggregate table. Forexample, transaction service provider system 102 may create a cohortmonthly aggregate table for each cohort level group (e.g., a firstcohort level group of a plurality of cohort groups of accounts). In suchan example, the cohort monthly aggregate table may include aggregatetransaction data associated with a plurality of transactions during thespecified event time period that is associated with a cohort level group(e.g., the first cohort level group).

In some non-limiting embodiments, transaction service provider system102 may identify each account of a cohort level group (e.g., a firstcohort level group) as either an existing customer or a new customer fora specified merchant. For example, transaction service provider system102 may identify each account of a cohort level group (e.g., a firstcohort level group) as being associated with an existing customer or asbeing associated with a new customer. In some non-limiting embodimentsor aspects, transaction service provider system 102 may identify eachaccount of the cohort level group (e.g., the first cohort level group)as being associated with an existing customer or as being associatedwith a new customer based on an indication (e.g., a flag) of whether theaccount has conducted a transaction with a merchant. Additionally, oralternatively, transaction service provider system 102 may identify eachaccount of the cohort level group (e.g., the first cohort level group)as either an existing customer or a new customer based on an indicationof whether the account has conducted a transaction with a merchantwithin a time period prior to the specific event time period associatedwith a cohort level group (e.g., a first cohort level group). Where anindication is present (e.g., a flag is set to true), transaction serviceprovider system 102 may identify the account as being associated with anexisting customer. Additionally, or alternatively, where an indicationis not present (e.g., a flag is set to false), transaction serviceprovider system 102 may identify the account as not being associatedwith an existing customer. In some non-limiting embodiments or aspects,an account associated with an existing customer may include an accountthat was involved in a transaction with a merchant within a time periodprior to the specific event time period associated with a cohort levelgroup of accounts. In some non-limiting embodiments or aspects, anaccount associated with a new customer may include an account that wasnot involved in a transaction with the merchant within a time periodprior to the specific event time period associated with a cohort levelgroup of accounts. Additionally or alternatively, the account associatedwith a new customer may include an account that was involved in atransaction with the merchant during the specific event time periodassociated with the cohort level group of accounts.

In some non-limiting embodiments or aspects, transaction serviceprovider system 102 may determine a sample of control accounts from thecombined plurality of accounts. For example, transaction serviceprovider system 102 may determine a sample of control accounts from thecombined plurality of accounts based on at least one random number(e.g., a random number, a plurality of random numbers, a plurality ofrandom numbers within a range of random numbers, a plurality of randomnumbers within a plurality of ranges of random numbers, and/or thelike). Additionally, or alternatively, transaction service providersystem 102 may determine the sample of control accounts from the firstcohort level group based on at least one random number (e.g., a randomnumber, a plurality of random numbers, a plurality of random numberswithin a range of random numbers, a plurality of random numbers within aplurality of ranges of random numbers, and/or the like) included in thecohort monthly aggregate table for the first cohort level group.Additionally, or alternatively, transaction service provider system 102may determine the sample of control accounts from a first cohort levelgroup based on at least one random number (e.g., a random number, aplurality of random numbers, a plurality of random numbers within arange of random numbers, a plurality of random numbers within aplurality of ranges of random numbers, and/or the like) included in eachcohort monthly aggregate table for each cohort level group (e.g., afirst cohort level group).

In some non-limiting embodiments or aspects, transaction serviceprovider system 102 may determine a plurality of modeling variables foraccounts of the combined plurality of accounts. For example, transactionservice provider system 102 may determine a plurality of modelingvariables for accounts of the combined plurality of accounts.Additionally, or alternatively, transaction service provider system 102may determine the plurality of modeling variables for accounts of theplurality of control accounts and accounts of the plurality of exposedaccounts. Additionally, or alternatively, transaction service providersystem 102 may determine the plurality of modeling variables foraccounts of the plurality of control accounts and accounts of theplurality of exposed accounts in a cohort level group (e.g., a firstcohort level group). In some non-limiting embodiments or aspects,modeling variables may include one or more of a spending amount (e.g., ameasure of funds transferred during one or more transactions) of a rangeof spending amounts (e.g., a low spending range, a middle spendingrange, an upper spending range, and/or the like), a number oftransactions initiated using an account of a customer that involve amerchant during a time period (e.g., two transactions, threetransactions, five transactions, and/or the like), a total transactionamount of transactions initiated using an account of a customer thatinvolve a merchant during a time period, a number of transactionsinitiated using an account of a customer that involve a merchant duringa time period that is prior to a time period (e.g., before an event timeperiod), a number of transactions initiated using an account of acustomer that involve a merchant during a time period that is after atime period (e.g., after an event time period), a total transactionamount of transactions initiated using an account of a customer thatinvolve a merchant during a time period that is prior to a time period,a total transaction amount of transactions initiated using an account ofa customer that involve a merchant during a time period that is after atime period involving a merchant initiated, a merchant category of amerchant involved in a transaction (e.g., a merchant category code), anamount spent at a merchant during a time period (e.g., an amount spentat a merchant during week, during a month, and/or the like), anindication of whether a transaction was initiated using a credit and/ora debit account of a customer that involves a merchant, an averagetransaction amount of a transaction initiated using an account of acustomer that involves a merchant during a time period, a percentage ofspending associated with a category according to a merchant category,and/or the like.

In some non-limiting embodiments or aspects, transaction serviceprovider system 102 may create a modeling table. For example,transaction service provider system 102 may create a modeling tablebased on a sample of control accounts associated with (e.g., includedin) the monthly aggregate transaction data table. In some non-limitingembodiments or aspects, the modeling table may include a plurality ofmodeling variables and/or an indication of whether each account is partof a validation segment of accounts.

As shown in FIG. 3 , at step 308, process 300 includes determining asegment level group. For example, transaction service provider system102 may determine a segment level group. In some non-limitingembodiments or aspects, transaction service provider system 102 maydetermine a plurality of segment level groups. In some non-limitingembodiments or aspects, transaction service provider system 102 maydetermine one or more segment level groups (e.g., a plurality of segmentlevel groups, a predetermined number of segment level groups, and/or thelike). In some non-limiting embodiments or aspects, transaction serviceprovider system 102 may determine the first segment level group from acohort level group (e.g., a first cohort level group). For example,transaction service provider system 102 may determine the first segmentlevel group of accounts from each cohort level group.

In some non-limiting embodiments or aspects, transaction serviceprovider system 102 may determine a segment level group (e.g., a firstsegment level group) of accounts from the cohort level group of accountsbased on whether the account is part of a validation segment of accountsor post-event segment of accounts and whether the account is an existingaccount or a new account. For example, transaction service providersystem 102 may determine a segment level group (e.g., first segmentlevel group of accounts from the cohort level group of accounts based onan indication of whether the account is part of a validation segment ofaccounts or post-event segment of accounts and an indication of whetherthe account is an existing account or a new account. In somenon-limiting embodiments or aspects, transaction service provider system102 may determine a subset of accounts from each cohort level groupbased on an indication of whether the account is part of a validationsegment of accounts. In some non-limiting embodiments or aspects,transaction service provider system 102 may determine that an accountmay not be part of a validation segment of accounts for a cohort levelgroup based on transaction service provider system 102 determining thatthe indication of whether the account is part of the validation segmentof accounts is not present. Additionally, or alternatively, transactionservice provider system 102 may determine that an account may be part ofthe validation segment of accounts for a cohort level group based ontransaction service provider system 102 determining that the indicationof whether the account is part of a validation segment of accounts ispresent. In some non-limiting embodiments or aspects, transactionservice provider system 102 may determine a subset of accounts from eachcohort level group based on an indication of whether the account is partof a post-event segment of accounts. In some non-limiting embodiments oraspects, transaction service provider system 102 may determine that anaccount may not be part of a post-event segment of accounts for a cohortlevel group based on transaction service provider system 102 determiningthat the indication of whether the account is part of the post-eventsegment of accounts is not present. Additionally, or alternatively,transaction service provider system 102 may determine that an accountmay be part of the post-event segment of accounts for a cohort levelgroup based on transaction service provider system 102 determining thatthe indication of whether the account is part of a post-event segment ofaccounts is present.

In some non-limiting embodiments or aspects, transaction serviceprovider system 102 may identify each account of the cohort level group.For example, transaction service provider system 102 may identify eachaccount of the cohort level group based on whether the account isassociated with an existing customer or a new customer. In somenon-limiting embodiments or aspects, transaction service provider system102 may identify each account of the cohort level group based on anindication of whether the account is associated with an existingcustomer or a new customer. In some non-limiting embodiments or aspects,transaction service provider system 102 may determine that an accountmay be associated with an existing customer based on transaction serviceprovider system 102 determining that the indication of whether theaccount is associated with an existing customer is present.Additionally, or alternatively, transaction service provider system 102may determine that an account may be associated with a new customerbased on transaction service provider system 102 determining that theindication of whether the account is associated with a new customer ispresent. In some non-limiting embodiments or aspects, each cohort levelgroup may include one or more subsets of accounts. For example, eachcohort level group may include four subsets of accounts. In such anexample, the four subsets of accounts may include a first subset for avalidation and existing segment of accounts, a second subset for avalidation and new segment of accounts, a third subset for anon-validation and existing segment of accounts, and a fourth subset fora non-validation and new segment of accounts.

As shown in FIG. 3 , at step 310, process 300 includes generating aprediction model. For example, transaction service provider system 102may generate a prediction model. In some non-limiting embodiments oraspects, transaction service provider system 102 may generate theprediction model based on one or more control accounts. For example,transaction service provider system 102 may generate the predictionmodel based on a sample of control accounts. In some non-limitingembodiments or aspects, transaction service provider system 102 maygenerate the prediction model based on a plurality of control accounts.For example, transaction service provider system 102 may generate theprediction model based on a plurality of control accounts in a cohortlevel group.

In some non-limiting embodiments or aspects, transaction serviceprovider system 102 may generate a prediction model based on a pluralityof modeling variables. For example, transaction service provider system102 may generate a prediction model based on a plurality of modelingvariables for control accounts. In such an example, transaction serviceprovider system 102 may generate the prediction model based on theplurality of modeling variables for the control accounts, where thecontrol accounts are included in a cohort level group. In somenon-limiting embodiments or aspects, the plurality of modeling variablesmay be included in a modeling table.

In some non-limiting embodiments or aspects, transaction serviceprovider system 102 may generate a prediction model by training a modelbased on a subset of a plurality of modeling variables. For example,transaction service provider system 102 may generate a prediction modelby training a model based on a subset of a plurality of modelingvariables and/or a small number (e.g., 3, 4, or 5) of randomly shuffledvariables when transaction service provider system 102 trains theprediction model. In some non-limiting embodiments or aspects,transaction service provider system 102 may determine variableimportance when transaction service provider system 102 trains theprediction model. In some non-limiting embodiments or aspects,transaction service provider system 102 may determine that one or morevariables have a lowest variable importance (e.g., are determined to beless important that one or more other variables). For example,transaction service provider system 102 may determine that one or morevariables have a lowest variable importance and transaction serviceprovider system 102 may exclude the one or more variables having thelowest variable importance. In some non-limiting embodiments or aspects,transaction service provider system 102 may train, validate, and testthe prediction model. For example, transaction service provider system102 may train, validate, and test the prediction model based onvariables that do not include the one or more variables having a lowestvariable importance.

In some non-limiting embodiments or aspects, transaction serviceprovider system 102 may calculate an error term. For example,transaction service provider system 102 may calculate an error termbased on (e.g., as part of) training a prediction model. In somenon-limiting embodiments or aspects, transaction service provider system102 may update weights of the prediction model. For example, transactionservice provider system 102 may update weights of the prediction modelbased on the error term. In some non-limiting embodiments or aspects,transaction service provider system 102 may calculate the error termbased on transaction service provider system 102 calculating a meansquared error, a sum of actuals, a sum of predictions, an estimatedlift, and/or out of bag R{circumflex over ( )}2 variables. For example,transaction service provider system 102 may calculate the error termbased on transaction service provider system 102 calculating a meansquared error, a sum of actuals, a sum of predictions, an estimatedlift, and/or out of bag R{circumflex over ( )}2 variables andtransaction service provider system 102 may update weights of theprediction model based on the error term.

In some non-limiting embodiments or aspects, transaction serviceprovider system 102 may generate a prediction score. For example,transaction service provider system 102 may generate a prediction scorefor one or more control accounts. In such an example, transactionservice provider system 102 may generate the prediction score for eachcontrol account of a plurality of control accounts. In some non-limitingembodiments or aspects, transaction service provider system 102 maygenerate the prediction score for each control account in a segmentlevel group of accounts. For example, transaction service providersystem 102 may generate the prediction score for each control account ineach segment level group of accounts in a cohort level group. In somenon-limiting embodiments or aspects, a prediction score may represent anamount of spending involving an account after the specified event timeperiod associated with a cohort group. For example, a prediction scoremay represent an amount of spending involving an account such as, forexample, a control account for which aggregate transaction dataassociated with the control account was used as input to the predictionmodel. Additionally, or alternatively, a prediction score may representthe amount of spending involving the account such as an exposed accountfor which aggregate transaction data associated with the exposed accountwas used as input to the prediction model. In some non-limitingembodiments or aspects, transaction service provider system 102 may addnoise to a prediction score of a control account included in a segmentlevel group of accounts.

In some non-limiting embodiments or aspects, transaction serviceprovider system 102 may generate a prediction score for one or moreexposed accounts. For example, transaction service provider system 102may generate a prediction score for each exposed account of a pluralityof exposed accounts. In some non-limiting embodiments or aspects,transaction service provider system 102 may generate the predictionscore for each exposed account in a segment level group of accounts. Forexample, transaction service provider system 102 may generate theprediction score for each exposed account in a segment level group ofaccounts of a cohort level group.

As shown in FIG. 3 , at step 312, process 300 includes determining acontrol account that corresponds to an exposed account using theprediction model. For example, transaction service provider system 102may determine a control account that corresponds to an exposed accountusing the prediction model. In some non-limiting embodiments or aspects,transaction service provider system 102 may determine a plurality ofcontrol accounts that correspond to an exposed account. For example,transaction service provider system 102 may determine a plurality ofcontrol accounts that correspond to an exposed account based on aprediction score for the control account and a prediction score for theexposed account. In some non-limiting embodiments or aspects,transaction service provider system 102 may determine a plurality ofprediction scores for a first plurality of control accounts.Additionally, or alternatively, transaction service provider system 102may determine a second plurality of control accounts that have aprediction score that is equal to a prediction score of an exposedaccount. Additionally, or alternatively, transaction service providersystem 102 may determine a second plurality of control accounts thathave a prediction score that is equal to a prediction score that iswithin a threshold of the prediction score of the exposed account. Insome non-limiting embodiments or aspects, transaction service providersystem 102 may determine the second plurality of control accounts thathave the prediction score that is equal to the prediction score that iswithin the threshold of the prediction score of the exposed accountbased on transaction service provider system 102 adding noise to theprediction score of a control account included in a segment level groupof accounts.

In some non-limiting embodiments or aspects, transaction serviceprovider system 102 may generate a plurality of clusters of controlaccounts. For example, transaction service provider system 102 maygenerate a plurality of clusters of control accounts for the secondplurality of control accounts. In some non-limiting embodiments oraspects, transaction service provider system 102 may generate theplurality of clusters using a K-means clustering algorithm. In somenon-limiting embodiments or aspects, each cluster may include oneexposed account. In some non-limiting embodiments or aspects,transaction service provider system 102 may generate the plurality ofclusters of control accounts based on modeling variables that representaggregate transaction data associated with the second plurality ofaccounts. For example, transaction service provider system 102 maygenerate the plurality of clusters of control accounts based on modelingvariables that represent aggregate transaction data associated with thesecond plurality of accounts, where the modeling variables are modelingvariables based on activities prior to the specified event time period.

In some non-limiting embodiments or aspects, transaction serviceprovider system 102 may determine one or more control accounts of eachcluster of control accounts. For example, transaction service providersystem 102 may determine one or more control accounts of each cluster ofcontrol accounts that correspond to the exposed account of the cluster.In such an example, transaction service provider system 102 maydetermine the one or more control accounts of each cluster of controlaccounts that correspond to the exposed account of the cluster using aK-nearest neighbor algorithm. In some non-limiting embodiments oraspects, transaction service provider system 102 may determine apropensity score for between each control account of the cluster ofcontrol accounts and the exposed account. For example, transactionservice provider system 102 may determine a propensity score for betweeneach control account of the cluster of control accounts and the exposedaccount using the K-nearest neighbor algorithm. In some non-limitingembodiments or aspects, transaction service provider system 102 maydetermine a propensity score for between each control account of thecluster of control accounts and the exposed account based on modelingvariables that represent aggregate transaction data associated with thesecond plurality of accounts. For example, transaction service providersystem 102 may determine a propensity score for between each controlaccount of the cluster of control accounts and the exposed account basedon modeling variables that represent aggregate transaction dataassociated with the second plurality of accounts, where the modelingvariables are modeling variables based on activities prior to thespecified event time period (e.g., modeling variables such as spendingamount (e.g., a measure of funds transferred during one or moretransactions)) of a range of spending amounts (e.g., a low spendingrange, a middle spending range, an upper spending range, and/or thelike), a number of transactions initiated using an account of a customerthat involve a merchant during a time period (e.g., two transactions,three transactions, five transactions, and/or the like), a totaltransaction amount of transactions initiated using an account of acustomer that involve a merchant during a time period, a number oftransactions initiated using an account of a customer that involve amerchant during a time period that is prior to a time period (e.g., anevent time period), a total transaction amount of transactions initiatedusing an account of a customer that involve a merchant during a timeperiod that is prior to a time period, a merchant category of a merchantinvolved in a transaction (e.g., a merchant category code), an amountspent at a merchant during a time period (e.g., an amount spent at amerchant during week, during a month, and/or the like), an indication ofwhether a transaction was initiated using a credit and/or a debitaccount of a customer that involves a merchant, an average transactionamount of a transaction initiated using an account of a customer thatinvolves a merchant during a time period, a percentage of spendingassociated with a category according to a merchant category, and/or thelike.

Although the present disclosure has been described in detail for thepurpose of illustration based on what is currently considered to be themost practical and preferred embodiments, it is to be understood thatsuch detail is solely for that purpose and that the present disclosureis not limited to the disclosed embodiments, but, on the contrary, isintended to cover modifications and equivalent arrangements that arewithin the spirit and scope of the present disclosure. For example, itis to be understood that the present disclosure contemplates that, tothe extent possible, one or more features of any embodiment can becombined with one or more features of any other embodiment.

What is claimed is:
 1. A system, comprising: at least one processor programmed or configured to: determine a first cohort level group of accounts, wherein the first cohort level group of accounts comprises a group of exposed accounts and a group of control accounts that are associated with a first specified event time period of a plurality of event time periods, wherein each account of the group of exposed accounts is associated with an event time period of a plurality of event time periods, wherein each event time period of the plurality of event time periods is associated with a time period during which the account was exposed to an event associated with a merchant, wherein the event associated with the merchant is an offer associated with a loyalty program of the merchant or enrollment in the loyalty program of the merchant, and wherein each account of the group of control accounts were not exposed to the event associated with the merchant and conducted at least one transaction involving the merchant during at least one event time period of the plurality of event time periods; determine a first segment level group of accounts from the first cohort level group of accounts based on an indication of whether each account of the first cohort level group of accounts is part of a validation segment of accounts or post-event segment of accounts and an indication of whether the account is an existing account or a new account; generate a prediction model based on a plurality of control accounts that are included in the first segment level group of accounts, wherein the prediction model is configured to output a prediction score that represents an amount of spending involving an account after the first specified event time period during which the account was exposed to the event associated with the merchant, wherein, when generating the prediction model, the at least one processor is programmed or configured to: determine a plurality of modeling variables for each account of the group of control accounts included in the first cohort level group of accounts, wherein the plurality of modeling variables comprises: a number of transactions initiated using the account that involve the merchant during the first specified event time period; determine a variable importance metric associated with each modeling variable of the plurality of modeling variables; exclude one or more modeling variables of the plurality of modeling variables based on the variable importance metric associated with the one or more modeling variables of the plurality of modeling variables to provide a subset of modeling variables; train the prediction model based on the subset of modeling variables; calculate an error term based on training the prediction model; and update one or more weights of the prediction model based on the error term; identify a first exposed account of a plurality of exposed accounts that are included in the first segment level group of accounts; and determine a first control account of the plurality of control accounts that are included in the first segment level group of accounts that corresponds to the first exposed account using the prediction model, wherein, when determining the first control account of the plurality of control accounts that are included in the first segment level group of accounts that corresponds to the first exposed account using the prediction model, the at least one processor is programmed or configured to: determine the first control account of the plurality of control accounts that are included in the first segment level group of accounts that corresponds to the first exposed account based on a prediction score for the first control account and a prediction score for the first exposed account provided by the prediction model.
 2. The system of claim 1, wherein the group of exposed accounts comprises a group of accounts that were exposed to a first event associated with the merchant during the first specified event time period, and wherein the group of control accounts comprises a group of accounts that conducted at least one transaction involving the merchant during the first specified event time period.
 3. The system of claim 1, wherein, when determining the first cohort level group of accounts, the at least one processor is programmed or configured to: determine a combined plurality of accounts, wherein, when determining the combined plurality of accounts, the at least one processor is programmed or configured to: determine, for each account of a plurality of accounts, a distance between a merchant location of the merchant associated with the event and an address associated with an account; determine that the distance between the merchant location of the merchant associated with the event and the address associated with the account of the plurality of accounts satisfies a threshold value of distance; and include the account in the combined plurality of accounts based on determining that the distance between the merchant location of the merchant associated with the event and the address associated with the account satisfies the threshold value of distance; and determine the first cohort level group of accounts based on the combined plurality of accounts.
 4. The system of claim 3, wherein the at least one processor is further programmed or configured to: determine, for each account of the combined plurality of accounts, aggregate transaction data associated with a plurality of transactions involving each account during a time interval; and wherein, when determining the first control account of the plurality of control accounts that are included in the first segment level group of accounts that corresponds to the first exposed account using the prediction model, the at least one processor is programmed or configured to: determine the first control account of the plurality of control accounts that are included in the first segment level group of accounts that corresponds to the first exposed account based on the aggregate transaction data associated with the plurality of transactions involving each account during the time interval.
 5. The system of claim 4, wherein when determining, for each account of the combined plurality of accounts, aggregate transaction data associated with the plurality of transactions involving each account during the time interval, the at least one processor is programmed or configured to: create an aggregate transaction data table that includes aggregate transaction data associated with the plurality of transactions during each event time period of a plurality of event time periods involving each account of the combined plurality of accounts; determine an initial event time period of the plurality of event time periods, wherein the initial event time period is a beginning of the time interval; determine a final event time period of the plurality of event time periods, wherein the final event time period is an end of the time interval; and determine, for each account of the combined plurality of accounts, aggregate transaction data associated with the plurality of transactions involving each account during the time interval based on aggregate transaction data associated with the plurality of transactions involving each account between the initial event time period and the final event time period.
 6. The system of claim 1, wherein the at least one processor is further programmed or configured to: determine cohort aggregate transaction data for each account of the first cohort level group, wherein the cohort aggregate transaction data includes aggregate transaction data associated with a plurality of transactions, which involve each account included in the group of exposed accounts and control accounts that are associated with the first specified event time period, during the first specified event time period associated with the first cohort level group.
 7. The system of claim 1, wherein the at least one processor is further programmed or configured to: identify each account of the first cohort level group as being associated with an existing customer or as being associated with a new customer.
 8. The system of claim 1, wherein when determining the first segment level group of accounts from the first cohort level group of accounts, the at least one processor is programmed or configured to: determine the first segment level group of accounts from the first cohort level group of accounts based on an indication of whether the account is part of a validation segment of accounts or post-event segment of accounts and an indication of whether the account is an existing account or a new account; and wherein when generating the prediction model based on the plurality of control accounts that are included in the first segment level group of accounts, the at least one processor is programmed or configured to: generate the prediction model based on the plurality of control accounts that are part of the post-event segment of accounts and that are existing accounts.
 9. A computer program product, the computer program product comprising at least one non-transitory computer-readable medium including one or more instructions that, when executed by at least one processor, cause the at least one processor to: determine a first cohort level group of accounts, wherein the first cohort level group of accounts comprises a group of exposed accounts and a group of control accounts that are associated with a first specified event time period of a plurality of event time periods, wherein each account of the group of exposed accounts is associated with an event time period of a plurality of event time periods, wherein each event time period of the plurality of event time periods is associated with a time period during which the account was exposed to an event associated with a merchant, wherein the event associated with the merchant is an offer associated with a loyalty program of the merchant or enrollment in the loyalty program of the merchant, and wherein each account of the group of control accounts were not exposed to the event associated with the merchant and conducted at least one transaction involving the merchant during at least one event time period of the plurality of event time periods; determine a first segment level group of accounts from the first cohort level group of accounts based on an indication of whether each account of the first cohort level group of accounts is part of a validation segment of accounts or post-event segment of accounts and an indication of whether the account is an existing account or a new account; generate a prediction model based on a plurality of control accounts that are included in the first segment level group of accounts, wherein the prediction model is configured to output a prediction score that represents an amount of spending involving an account after the first specified event time period during which the account was exposed to the event associated with the merchant, wherein, when generating the prediction model, the at least one processor is programmed or configured to: determine a plurality of modeling variables for each account of the group of control accounts included in the first cohort level group of accounts, wherein the plurality of modeling variables comprises: a number of transactions initiated using the account that involve the merchant during the first specified event time period; determine a variable importance metric associated with each modeling variable of the plurality of modeling variables; exclude one or more modeling variables of the plurality of modeling variables based on the variable importance metric associated with the one or more modeling variables of the plurality of modeling variables to provide a subset of modeling variables; train the prediction model based on the subset of modeling variables; calculate an error term based on training the prediction model; and update one or more weights of the prediction model based on the error term; identify a first exposed account of a plurality of exposed accounts that are included in the first segment level group of accounts; and determine a first control account of the plurality of control accounts that are included in the first segment level group of accounts that corresponds to the first exposed account using the prediction model, wherein the one or more instructions that cause the at least one processor to determine the first control account of the plurality of control accounts that are included in the first segment level group of accounts that corresponds to the first exposed account using the prediction model, cause the at least one processor to: determine the first control account of the plurality of control accounts that are included in the first segment level group of accounts that corresponds to the first exposed account based on a prediction score for the first control account and a prediction score for the first exposed account provided by the prediction model.
 10. The computer program product of claim 9, wherein the group of exposed accounts comprises a group of accounts that were exposed to a first event associated with the merchant during the first specified event time period, and wherein the group of control accounts comprises a group of accounts that conducted at least one transaction involving the merchant during the first specified event time period.
 11. The computer program product of claim 9, wherein the one or more instructions that cause the at least one processor to determine the first cohort level group of accounts, cause the at least one processor to: determine a combined plurality of accounts, wherein, the one or more instructions that cause the at least one processor to determine the combined plurality of accounts, cause the at least one processor to: determine, for each account of a plurality of accounts, a distance between a merchant location of the merchant associated with the event and an address associated with an account; determine that the distance between the merchant location of the merchant associated with the event and the address associated with the account of the plurality of accounts satisfies a threshold value of distance; and include the account in the combined plurality of accounts based on determining that the distance between the merchant location of the merchant associated with the event and the address associated with the account satisfies the threshold value of distance; and determine the first cohort level group of accounts based on the combined plurality of accounts.
 12. The computer program product of claim 11, wherein the one or more instructions further cause the at least one processor to: determine, for each account of the combined plurality of accounts, aggregate transaction data associated with a plurality of transactions involving each account during a time interval; and wherein, the one or more instructions that cause the at least one processor to determine the first control account of the plurality of control accounts that are included in the first segment level group of accounts that corresponds to the first exposed account using the prediction model, cause the at least one processor to: determine the first control account of the plurality of control accounts that are included in the first segment level group of accounts that corresponds to the first exposed account based on the aggregate transaction data associated with the plurality of transactions involving each account during the time interval.
 13. The computer program product of claim 12, wherein the one or more instructions that cause the at least one processor to determine, for each account of the combined plurality of accounts, aggregate transaction data associated with the plurality of transactions involving each account during the time interval, cause the at least one processor to: create an aggregate transaction data table that includes aggregate transaction data associated with a plurality of transactions during each event time period of a plurality of event time periods involving each account of the combined plurality of accounts; determine an initial event time period of the plurality of event time periods, wherein the initial event time period is a beginning of the time interval; determine a final event time period of the plurality of event time periods, wherein the final event time period is an end of the time interval; and determine, for each account of the combined plurality of accounts, aggregate transaction data associated with the plurality of transactions involving each account during the time interval based on aggregate transaction data associated with the plurality of transactions involving each account between the initial event time period and the final event time period.
 14. The computer program product of claim 9, wherein the one or more instructions further cause the at least one processor to: determine cohort aggregate transaction data for each account of the first cohort level group, wherein the cohort aggregate transaction data includes aggregate transaction data associated with a plurality of transactions, which involve each account included in the group of exposed accounts and control accounts that are associated with the first specified event time period, during the first specified event time period associated with the first cohort level group.
 15. The computer program product of claim 9, wherein, the one or more instructions that cause the at least one processor to determine the first segment level group of accounts from the first cohort level group of accounts, cause the at least one processor to: determine the first segment level group of accounts from the first cohort level group of accounts based on an indication of whether the account is part of a validation segment of accounts or post-event segment of accounts and an indication of whether the account is an existing account or a new account; and wherein, the one or more instructions that cause the at least one processor to generate the prediction model based on the plurality of control accounts that are included in the first segment level group of accounts, cause the at least one processor to: generate the prediction model based on the plurality of control accounts that are part of the post-event segment of accounts and that are existing accounts.
 16. A method, comprising: determining, with at least one processor, a first cohort level group of accounts, wherein the first cohort level group of accounts comprises a group of exposed accounts and a group of control accounts that are associated with a first specified event time period of a plurality of event time periods, wherein each account of the group of exposed accounts is associated with an event time period of a plurality of event time periods, wherein each event time period of the plurality of event time periods is associated with a time period during which the account was exposed to an event associated with a merchant, wherein the event associated with the merchant is an offer associated with a loyalty program of the merchant or enrollment in the loyalty program of the merchant, and wherein each account of the group of control accounts were not exposed to the event associated with the merchant and conducted at least one transaction involving the merchant during at least one event time period of the plurality of event time periods; determining, with at least one processor, a first segment level group of accounts from the first cohort level group of accounts based on an indication of whether each account of the first cohort level group of accounts is part of a validation segment of accounts or post-event segment of accounts and an indication of whether the account is an existing account or a new account; generating, with at least one processor, a prediction model based on a plurality of control accounts that are included in the first segment level group of accounts, wherein the prediction model is configured to output a prediction score that represents an amount of spending involving an account after the first specified event time period during which the account was exposed to the event associated with the merchant, wherein generating the prediction model comprises: determining a plurality of modeling variables for each account of the group of control accounts included in the first cohort level group of accounts, wherein the plurality of modeling variables comprises: a number of transactions initiated using the account that involve the merchant during the first specified event time period; determining a variable importance metric associated with each modeling variable of the plurality of modeling variables; excluding one or more modeling variables of the plurality of modeling variables based on the variable importance metric associated with the one or more modeling variables of the plurality of modeling variables to provide a subset of modeling variables; training the prediction model based on the subset of modeling variables; calculating an error term based on training the prediction model; and updating one or more weights of the prediction model based on the error term; identifying, with at least one processor, a first exposed account of a plurality of exposed accounts that are included in the first segment level group of accounts; and determining, with at least one processor, a first control account of the plurality of control accounts that are included in the first segment level group of accounts that corresponds to the first exposed account using the prediction model, wherein determining the first control account of the plurality of control accounts that are included in the first segment level group of accounts that corresponds to the first exposed account using the prediction model comprises: determining the first control account of the plurality of control accounts that are included in the first segment level group of accounts that corresponds to the first exposed account based on a prediction score for the first control account and a prediction score for the first exposed account provided by the prediction model.
 17. The method of claim 16, wherein determining the first cohort level group of accounts comprises: determining a combined plurality of accounts, wherein determining the combined plurality of accounts comprises: determining, for each account of a plurality of accounts, a distance between a merchant location of the merchant associated with the event and an address associated with an account; determining that the distance between the merchant location of the merchant associated with the event and the address associated with the account of the plurality of accounts satisfies a threshold value of distance; and including the account in the combined plurality of accounts based on determining that the distance between the merchant location of the merchant associated with the event and the address associated with the account satisfies the threshold value of distance; and determining the first cohort level group of accounts based on the combined plurality of accounts.
 18. The method of claim 17, further comprising: determining, for each account of the combined plurality of accounts, aggregate transaction data associated with a plurality of transactions involving each account during a time interval; and wherein determining the first control account of the plurality of control accounts that are included in the first segment level group of accounts that corresponds to the first exposed account using the prediction model comprises: determining the first control account of the plurality of control accounts that are included in the first segment level group of accounts that corresponds to the first exposed account based on the aggregate transaction data associated with the plurality of transactions involving each account during the time interval.
 19. The method of claim 18, wherein determining, for each account of the combined plurality of accounts, aggregate transaction data associated with the plurality of transactions involving each account during the time interval comprises: creating an aggregate transaction data table that includes aggregate transaction data associated with the plurality of transactions during each event time period of a plurality of event time periods involving each account of the combined plurality of accounts; determining an initial event time period of the plurality of event time periods, wherein the initial event time period is a beginning of the time interval; determining a final event time period of the plurality of event time periods, wherein the final event time period is an end of the time interval; and determining, for each account of the combined plurality of accounts, aggregate transaction data associated with the plurality of transactions involving each account during the time interval based on aggregate transaction data associated with the plurality of transactions involving each account between the initial event time period and the final event time period.
 20. The method of claim 16, wherein determining the first segment level group of accounts from the first cohort level group of accounts comprises: determining the first segment level group of accounts from the first cohort level group of accounts based on an indication of whether the account is part of a validation segment of accounts or post-event segment of accounts and an indication of whether the account is an existing account or a new account; and wherein generating the prediction model based on the plurality of control accounts that are included in the first segment level group of accounts comprises: generating the prediction model based on the plurality of control accounts that are part of the post-event segment of accounts and that are existing accounts. 